Optimization of Injection Well Placement for Waterflooding in Heterogeneous Reservoirs using Artificial Neural Networks Coupled with Reservoir Simulation

dc.contributor.advisorLee, Kyung Jae
dc.contributor.committeeMemberSoliman, Mohamed Y.
dc.contributor.committeeMemberSakhaee-Pour, Ahmad
dc.creatorXiong, Xinwei
dc.date.accessioned2020-01-04T03:52:10Z
dc.date.createdMay 2019
dc.date.issued2019-05
dc.date.submittedMay 2019
dc.date.updated2020-01-04T03:52:10Z
dc.description.abstractSecondary recovery methods such as waterflooding and gasflooding are often applied to depleted reservoirs for enhancing oil and gas production. Reservoir simulations are performed to predict the hydrocarbon production by secondary recovery methods in heterogeneous fields. Given that a large number of discretized elements are required in simulations, it is usually not technically-and-economically feasible to run full-physics simulation for every possible case. In this regard, machine learning technology is introduced to predict the hydrocarbon production efficiently. In this paper, we firstly review the previous works on the heterogeneous reservoir simulations and the applications of Artificial Neural Network (ANN) models to predict the reservoir responses. Secondly, we present an improved data-driven models using ANN for the prediction of hydrocarbon production by waterflooding in heterogeneous reservoirs. It is proposed to improve the prediction performance while reducing the monitoring cost. Injection well placement can be optimized by using the proposed ANN models.
dc.description.departmentPetroleum Engineering, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/10657/5784
dc.language.isoeng
dc.rightsThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectReservoir simulation
dc.subjectHeterogeneous Reservoir
dc.subjectMachine learning
dc.subjectArtificial neural networks
dc.titleOptimization of Injection Well Placement for Waterflooding in Heterogeneous Reservoirs using Artificial Neural Networks Coupled with Reservoir Simulation
dc.type.dcmiText
dc.type.genreThesis
local.embargo.lift2021-05-01
local.embargo.terms2021-05-01
thesis.degree.collegeCullen College of Engineering
thesis.degree.departmentPetroleum Engineering, Department of
thesis.degree.disciplinePetroleum Engineering
thesis.degree.grantorUniversity of Houston
thesis.degree.levelMasters
thesis.degree.nameMaster of Science in Petroleum Engineering

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